抑郁症在言语中的表现与用于表示和识别说话者身份的特征重叠
Sri Harsha Dumpala1,2, Katerina Dikaios3,4, Sebastian Rodriguez1,2
1Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.
Scientific reports
|July 10, 2023
概括
语音嵌入式,来自语音模式,增强抑郁症检测和严重程度估计从言语. 然而,抑郁症严重程度的变化可能会影响说话者识别的准确性.
科学领域:
- 计算语言学计算语言学
- 临床心理学 临床心理学
- 语音处理 语音处理
背景情况:
- 语音分析用于语音识别,并且越来越多地用于检测抑郁症等医疗状况.
- 个人身份和抑郁症表现的语音特征之间的重叠并未得到充分理解.
研究的目的:
- 测试扬声器嵌入是否可以改善抑郁症检测和严重程度估计.
- 检查抑郁症严重程度的变化是否会干扰说话者识别.
主要方法:
- 从预先训练的模型中提取了嵌入式扬声器.
- 在临床 (DAIC-WOZ) 和自发语音 (VocalMind) 数据集上评估了具有声学特征的嵌入 (OpenSMILE).
- 评估了抑郁症检测和严重程度的估计,并通过纵向数据对扬声器识别的影响.
主要成果:
- 与单个方法相比,组合的扬声器嵌入和声学特征改善了严重性估计 (RMSE 6.01-6.28).
- 单独的扬声器嵌入器在抑郁症检测方面已经超过了最先进的技术 (BAc 66%-64%).
- 抑郁症严重程度的变化影响了扬声器识别,表明声空间的重叠.
结论:
- 嵌入式扬声器显著提高了抑郁症的检测和严重程度的估计.
- 抑郁症和个人身份有着共同的声学特征.
- 情绪波动会影响扬声器验证系统的可靠性.
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